The field of the invention relates generally to predicting consumer behavior from transaction card purchases and, more particularly, to network-based methods and systems for predicting whether a consumer will experience a life event, and predicting future purchases of the consumer based on the predicted life event.
Historically, the use of “charge” or transaction cards or payment cards for consumer transaction payments was at most regional and based on relationships between local credit or debit card issuing banks and various local merchants. The transaction card industry has since evolved with the issuing banks forming associations or networks (e.g., MasterCard®) and involving third party transaction processing companies (e.g., “Merchant Acquirers”) to enable cardholders to widely use transaction cards at any merchant's establishment, regardless of the merchant's banking relationship with the card issuer. (MasterCard is a registered trademark of MasterCard International Incorporated located in Purchase, N.Y.).
For example, FIG. 1 shows an exemplary multi-party payment card industry system for enabling payment-by-card transactions in which the merchants and issuer do not need to have a one-to-one special relationship. Yet, various scenarios exist in the payment-by-card industry today, where the card issuer has a special or customized relationship with a specific merchant, or group of merchants. These special or customized relationships may, for example, include private label programs, co-brand programs, proprietary card brands, rewards programs, and others.
Further, many merchants spend large amounts of money on marketing. Because marketing to a large general audience may be expensive, it may be advantageous to determine consumer interest in advance and attempt to target marketing toward consumers who are more likely to be interested in the product or products that a particular merchant sells. In this way merchants may attempt to better utilize their marketing budget to improve sales. In addition, consumers will be less likely to receive irrelevant or uninteresting offers.
At least some known systems and methods for determining consumer interest have relied on demographic information, such as age, income, and/or occupation. However, a consumer's needs may change quickly based on the consumer's current circumstances and/or the consumer's knowledge of future circumstances. For example, a consumer may decide to purchase a house in the near future. As such, the circumstances driving the consumer's decision to purchase the house may change faster than the ability of any of the known systems to determine such a change in demographic information. In other words, the demographic data of the consumer may change resulting in many purchases by the consumer, but by the time the change is detected by the known systems, the consumer has already made many of the purchases. In these cases, many of these purchases are then missed by the marketer.
In some other cases, there may be little or no change in demographic data although circumstances surrounding the consumer have changed and, accordingly, the consumer's needs have changed. For example, it may be more likely that a consumer who is planning to buy a house will need a mortgage and/or new furniture. As such, even though the consumer's demographic data, such as age, income, and/or occupation, may have remained constant, it is probable that the consumer will take out a mortgage and/or buy new furniture in the near future because of an impending home purchase.
However, in some cases, demographic data does help to predict some purchases. For example, if a person's job includes frequent relocation, that person may be a frequent home purchaser. Based on the demographic data alone, it may be possible to predict that the person is likely to buy a home.
At least some known targeting models have been known to achieve lifts of 1.3 times to 1.6 times on large populations. As used herein, the term “lift” refers to a ratio of positive responses to an offer by a consumer included within a target subgroup as compared to positive responses to the same offer made to the population as a whole. The target subgroup is usually selected to include those members of the whole population that are more likely to respond. It may, however, be difficult to determine the demographic information. This may be especially likely when changes have recently occurred in the person's demographic data.
Additionally, even when correct demographic information is known, it may be difficult to determine when a purchase, such as a home purchase, will actually occur. Timing is important in advertising and/or marketing because an unneeded advertisement and/or coupon might be thrown away, thus, wasting marketing money spent by the merchant. In one example, a discounted mortgage offer from bank X might be discarded by a consumer this month, while a discounted mortgage offer from bank Y might be used by the consumer next month because it is received near the time of a home purchase. As such, if bank Y is better at predicting when a mortgage is needed by a consumer, bank Y may be able to get more business than bank X. Further, the resources of bank X may be wasted by sending mortgage offers to consumers not planning on buying a new home, and the consumers may ignore possibly relevant offers after receiving many irrelevant offers. As such, matching offers to a consumer or a specific group of consumers in a timely fashion may be beneficial for the parties involved.
Accordingly, it is desirable to have the ability to identify indicators and/or signals that suggest a change in a consumer's needs or behaviors. By determining a consumer's changing needs and behaviors more accurately a merchant may, for example, be able to better predict what promotion, offers, and/or coupons to send to a consumer, and when these promotions, offers, and/or coupons should be sent to the consumer.